The Virtual Block Interface: A Flexible Alternative to the Conventional Virtual Memory Framework

Computers continue to diversify with respect to system designs, emerging memory technologies, and application memory demands. Unfortunately, continually adapting the conventional virtual memory framework to each possible system configuration is challenging, and often results in performance loss or requires non-trivial workarounds. To address these challenges, we propose a new virtual memory framework, the Virtual Block Interface (VBI). We design VBI based on the key idea that delegating memory management duties to hardware can reduce the overheads and software complexity associated with virtual memory. VBI introduces a set of variable-sized virtual blocks (VBs) to applications. Each VB is a contiguous region of the globally-visible VBI address space, and an application can allocate each semantically meaningful unit of information (e.g., a data structure) in a separate VB. VBI decouples access protection from memory allocation and address translation. While the OS controls which programs have access to which VBs, dedicated hardware in the memory controller manages the physical memory allocation and address translation of the VBs. This approach enables several architectural optimizations to (1) efficiently and flexibly cater to different and increasingly diverse system configurations, and (2) eliminate key inefficiencies of conventional virtual memory. We demonstrate the benefits of VBI with two important use cases: (1) reducing the overheads of address translation (for both native execution and virtual machine environments), as VBI reduces the number of translation requests and associated memory accesses; and (2) two heterogeneous main memory architectures, where VBI increases the effectiveness of managing fast memory regions. For both cases, VBI significanttly improves performance over conventional virtual memory

Linux kernel compaction through cold code swapping

There is a growing trend to use general-purpose operating systems like Linux in embedded systems. Previous research focused on using compaction and specialization techniques to adapt a general-purpose OS to the memory-constrained environment, presented by most, embedded systems. However, there is still room for improvement: it has been shown that even after application of the aforementioned techniques more than 50% of the kernel code remains unexecuted under normal system operation. We introduce a new technique that reduces the Linux kernel code memory footprint, through on-demand code loading of infrequently executed code, for systems that support virtual memory. In this paper, we describe our general approach, and we study code placement algorithms to minimize the performance impact of the code loading. A code, size reduction of 68% is achieved, with a 2.2% execution speedup of the system-mode execution time, for a case study based on the MediaBench II benchmark suite

Near-Memory Address Translation

Memory and logic integration on the same chip is becoming increasingly cost effective, creating the opportunity to offload data-intensive functionality to processing units placed inside memory chips. The introduction of memory-side processing units (MPUs) into conventional systems faces virtual memory as the first big showstopper: without efficient hardware support for address translation MPUs have highly limited applicability. Unfortunately, conventional translation mechanisms fall short of providing fast translations as contemporary memories exceed the reach of TLBs, making expensive page walks common. In this paper, we are the first to show that the historically important flexibility to map any virtual page to any page frame is unnecessary in today's servers. We find that while limiting the associativity of the virtual-to-physical mapping incurs no penalty, it can break the translate-then-fetch serialization if combined with careful data placement in the MPU's memory, allowing for translation and data fetch to proceed independently and in parallel. We propose the Distributed Inverted Page Table (DIPTA), a near-memory structure in which the smallest memory partition keeps the translation information for its data share, ensuring that the translation completes together with the data fetch. DIPTA completely eliminates the performance overhead of translation, achieving speedups of up to 3.81x and 2.13x over conventional translation using 4KB and 1GB pages respectively.Comment: 15 pages, 9 figure

Compressing High-Dimensional Data Spaces Using Non-Differential Augmented Vector Quantization

query processing times and space requirements. Database compression has been discovered to alleviate the I/O bottleneck, reduce disk space, improve disk access speed, speed up query, reduce overall retrieval time and increase the effective I/O bandwidth. However, random access to individual tuples in a compressed database is very difficult to achieve with most available compression techniques. We propose a lossless compression technique called non-differential augmented vector quantization, a close variant of the novel augmented vector quantization. The technique is applicable to a collection of tuples and especially effective for tuples with many low to medium cardinality fields. In addition, the technique supports standard database operations, permits very fast random access and atomic decompression of tuples in large collections. The technique maps a database relation into a static bitmap index cached access structure. Consequently, we were able to achieve substantial savings in space by storing each database tuple as a bit value in the computer memory. Important distinguishing characteristics of our technique is that individual tuples can be compressed and decompressed, rather than a full page or entire relation at a time, (b) the information needed for tuple compression and decompression can reside in the memory or at worst in a single page. Promising application domains include decision support systems, statistical databases and life databases with low cardinality fields and possibly no text field